A spatiotemporal weighted regression model (STWR v1.0) for analyzing local nonstationarity in space and time

Que, Xiang; Ma, Xiaogang; Ma, Chao; Chen, Qiyu

doi:https://doi.org/10.5194/gmd-13-6149-2020

Articles | Volume 13, issue 12

https://doi.org/10.5194/gmd-13-6149-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-13-6149-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 13, issue 12

Model description paper

|

03 Dec 2020

Model description paper |

| 03 Dec 2020

A spatiotemporal weighted regression model (STWR v1.0) for analyzing local nonstationarity in space and time

Xiang Que, Xiaogang Ma, Chao Ma, and Qiyu Chen

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Final revised paper (published on 03 Dec 2020)
Preprint (discussion started on 18 Mar 2020)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Comments', Anonymous Referee #1, 14 May 2020
- AC1: 'Revisions to address the comment', Xiaogang Ma, 02 Jul 2020
  - RC2: 'Review: second round', Anonymous Referee #1, 13 Jul 2020
RC3: 'Comment on A Spatiotemporal Weighted Regression Model (STWRv1.0) for Analyzing Local Non-stationarity in Space and Time', Anonymous Referee #2, 23 Jul 2020
- AC2: 'Revisions addressing Reviewer2's comments', Xiaogang Ma, 06 Aug 2020
- AC3: 'Additional information about the revised manuscript', Xiaogang Ma, 06 Aug 2020

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Xiaogang Ma on behalf of the Authors (21 Aug 2020) Author's response Manuscript

ED: Referee Nomination & Report Request started (11 Sep 2020) by Wolfgang Kurtz

RR by Anonymous Referee #2 (13 Sep 2020)

ED: Publish subject to minor revisions (review by editor) (29 Sep 2020) by Wolfgang Kurtz

AR by Xiaogang Ma on behalf of the Authors (08 Oct 2020) Author's response Manuscript

ED: Publish as is (23 Oct 2020) by Wolfgang Kurtz

AR by Xiaogang Ma on behalf of the Authors (25 Oct 2020)

Short summary

This paper presents a spatiotemporal weighted regression (STWR) model for exploring nonstationary spatiotemporal processes in nature and socioeconomics. A value change rate is introduced in the temporal kernel, which presents significant model fitting and accuracy in both simulated and real-world data. STWR fully incorporates observed data in the past and outperforms geographic temporal weighted regression (GTWR) and geographic weighted regression (GWR) models in several experiments.